{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/faster-k-means-cluster-estimation","title":"Faster K-Means Cluster Estimation","arxiv_id":"1701.04600","date":"2017-01-17","proceeding":null,"authors":["Siddhesh Khandelwal","Amit Awekar"],"abstract":"There has been considerable work on improving popular clustering algorithm\n`K-means' in terms of mean squared error (MSE) and speed, both. However, most\nof the k-means variants tend to compute distance of each data point to each\ncluster centroid for every iteration. We propose a fast heuristic to overcome\nthis bottleneck with only marginal increase in MSE. We observe that across all\niterations of K-means, a data point changes its membership only among a small\nsubset of clusters. Our heuristic predicts such clusters for each data point by\nlooking at nearby clusters after the first iteration of k-means. We augment\nwell known variants of k-means with our heuristic to demonstrate effectiveness\nof our heuristic. For various synthetic and real-world datasets, our heuristic\nachieves speed-up of up-to 3 times when compared to efficient variants of\nk-means.","url_abs":"http://arxiv.org/abs/1701.04600v1","url_pdf":"http://arxiv.org/pdf/1701.04600v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"faster-k-means-cluster-estimation","repo_url":"https://github.com/siddheshk/Faster-Kmeans","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}